mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 23:00:51 +08:00
a38968f098
- Validation errors that occur early in the lifecycle of prompt execution now get propagated to their callers in the EmbeddedComfyClient. This includes error messages about missing node classes. - The execution context now includes the node_id and the prompt_id - Latent previews are now sent with a node_id. This is not backwards compatible with old frontends. - Dependency execution errors are now modeled correctly. - Distributed progress encodes image previews with node and prompt IDs. - Typing for models - The frontend was updated to use node IDs with previews - Improvements to torch.compile experiments - Some controlnet_aux nodes were upstreamed
60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
import pytest
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from comfy import model_management
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from comfy.model_patcher import ModelPatcher
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from comfy.nodes.base_nodes import UNETLoader, CheckpointLoaderSimple
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from comfy_extras.nodes.nodes_torch_compile import QuantizeModel
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has_torchao = True
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try:
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from torchao.quantization import quantize_, int8_dynamic_activation_int8_weight
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except (ImportError, ModuleNotFoundError):
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has_torchao = False
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has_tensorrt = True
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try:
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from comfyui_tensorrt import STATIC_TRT_MODEL_CONVERSION
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except (ImportError, ModuleNotFoundError):
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has_tensorrt = False
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@pytest.fixture(scope="function", params=["flux1-dev.safetensors"])
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def model_patcher_obj(request) -> ModelPatcher:
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checkpoint_name = request.param
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model_obj = None
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try:
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if "flux" in checkpoint_name:
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model_obj, = UNETLoader().load_unet(checkpoint_name, weight_dtype="default")
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yield model_obj
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else:
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objs = CheckpointLoaderSimple().load_checkpoint(checkpoint_name)
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model_obj = objs[0]
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yield model_obj
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finally:
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model_management.unload_all_models()
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if model_obj is not None:
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model_obj.unpatch_model()
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del model_obj
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model_management.soft_empty_cache(force=True)
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@pytest.mark.forked
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@pytest.mark.skipif(not has_torchao, reason="torchao not installed")
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@pytest.mark.skipif(True, reason="wip")
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async def test_unit_torchao(model_patcher_obj):
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quantize_(model_patcher_obj.diffusion_model, int8_dynamic_activation_int8_weight(), device=model_management.get_torch_device())
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@pytest.mark.forked
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@pytest.mark.parametrize("strategy", ["torchao", "torchao-autoquant"])
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@pytest.mark.skipif(True, reason="wip")
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async def test_torchao_node(model_patcher_obj, strategy):
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QuantizeModel().execute(model_patcher_obj, strategy=strategy)
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@pytest.mark.forked
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@pytest.mark.skipif(True, reason="wip")
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async def test_tensorrt(model_patcher_obj):
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STATIC_TRT_MODEL_CONVERSION().convert(model_patcher_obj, "test", 1, 1024, 1024, 1, 14)
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